By Sunny Mahajan
Alpha research involves processing vast amounts of raw data. Several algorithms and techniques from engineering disciplines such as machine learning and digital signal processing can be leveraged to extract useful patterns, relationships, and features for alpha generation.
We frequently deal with weak classifiers/predictors in alpha research. The concept of boosting, from machine learning, can be utilized in many cases to create a strong learner out of several weak learners, by learning a suitable weighting function over the weak learners.
Alpha research also involves working with time series data. The concept of filtering, from digital signal processing, is useful in denoising time series data and decomposing time series into trend and cycle components.
Another technique of interest in alpha research is that of feature extraction. Algorithms such as principal component analysis (PCA) help to reduce the dimensionality of the feature space.
Now, let us look at each of these techniques individually to determine their use in generating alpha.
Boosting is based on the idea of creating a highly accurate prediction rule by combining many relatively weak and inaccurate rules. One of the most widely studied and used boosting algorithms is AdaBoost, as discussed by Freund and Schapire (1999).
AdaBoost learns a strong classifier by combining several weak classifiers using a weighing function, which is ...